Didrpg2emtl_comp.rar ❲HOT❳

The network focuses on learning the "rain residual" (the difference between the rainy image and the clean background), making the training process more stable and effective. Content of the .rar File

Settings for hyperparameters and directory paths used during the "comp" (computation/comparison) phase of the research. Performance and Impact DIDRPG2EMTL_comp.rar

The DID-RPG approach is notable for achieving a high and Structural Similarity Index (SSIM) compared to older methods like DDN (Deep Detail Network). It effectively preserves the background textures while removing both heavy and light rain streaks. The network focuses on learning the "rain residual"

Code to run the de-rainer on the provided sample "Rain200L" or "Rain200H" datasets. DIDRPG2EMTL_comp.rar